THE COMPARISON / A CLOSER LOOK
Aide by CodeStory vs Poolside
Compare the listed plans, useful capabilities, and practical tradeoffs.
Prices and features reflect editorial records. Check vendor terms before you buy; documentary checks do not imply hands-on testing.
Aide by CodeStory
🔴DeveloperAI Coding Assistants
Open-source AI-native IDE forked from VS Code that pairs a local agent (Sidecar) with cloud models to do proactive, multi-step coding.
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Starting Price
$0Poolside
🔴DeveloperAI Coding Assistants
Enterprise AI coding platform built on proprietary foundation models fine-tuned via reinforcement learning from code execution, deployed inside your VPC.
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Starting Price
Contact salesFeature Comparison
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Aide by CodeStory - Pros & Cons
Pros
- ✓Provides aI-native VS Code fork with an autonomous agent runtime (Sidecar), a concrete advantage for teams that need this workflow
- ✓Provides repository-wide planning and multi-file edits, a concrete advantage for teams that need this workflow
- ✓Provides local codebase indexing for privacy-friendlier context, a concrete advantage for teams that need this workflow
- ✓Provides model-agnostic: Claude, GPT, Gemini, OpenRouter, a concrete advantage for teams that need this workflow
Cons
- ✗Current vendor pricing and plan limits could not be independently verified because the site returned no usable HTML
- ✗Adoption requires a realistic pilot because behavior may differ by plan, deployment, or connected service
- ✗Automated output still needs human review, narrow permissions, and a tested recovery path
- ✗Total cost may include implementation, training, model usage, hosting, and support beyond the license price
Poolside - Pros & Cons
Pros
- ✓One of the only competitive AI coding platforms with true on-prem/VPC deployment
- ✓Custom-trained models designed for coding, not general-purpose instruct-tuned
- ✓Fits Fortune 500 procurement and compliance workflows out of the box
- ✓Full-stack platform — not just an inline autocomplete like early Copilot
Cons
- ✗Enterprise-only — no self-serve tier, no individual pricing
- ✗Long procurement cycles typical of on-prem AI deployments
- ✗Model quality claims (vs. Claude/GPT frontier) require internal benchmarking to validate
- ✗Requires substantial GPU infrastructure inside the customer's perimeter
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